Ilias Gatos1, Stavros Tsantis1, Stavros Spiliopoulos2, Dimitris Karnabatidis3, Ioannis Theotokas4, Pavlos Zoumpoulis4, Thanasis Loupas5, John D Hazle6, George C Kagadis7. 1. Department of Medical Physics, School of Medicine, University of Patras, Rion GR 26504, Greece. 2. Department of Radiology, School of Medicine, University of Athens, Athens GR 12461, Greece. 3. Department of Radiology, School of Medicine, University of Patras, Patras GR 26504, Greece. 4. Diagnostic Echotomography SA, 317C Kifissias Avenue, Kifissia GR 14561, Greece. 5. SuperSonic Imagine SA, Aix-en-Provence FR 13857, France. 6. Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030. 7. Department of Medical Physics, School of Medicine, University of Patras, Rion GR 26504, Greece and Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030.
Abstract
PURPOSE: Classify chronic liver disease (CLD) from ultrasound shear-wave elastography (SWE) imaging by means of a computer aided diagnosis (CAD) system. METHODS: The proposed algorithm employs an inverse mapping technique (red-green-blue to stiffness) to quantify 85 SWE images (54 healthy and 31 with CLD). Texture analysis is then applied involving the automatic calculation of 330 first and second order textural features from every transformed stiffness value map to determine functional features that characterize liver elasticity and describe liver condition for all available stages. Consequently, a stepwise regression analysis feature selection procedure is utilized toward a reduced feature subset that is fed into the support vector machines (SVMs) classification algorithm in the design of the CAD system. RESULTS: With regard to the mapping procedure accuracy, the stiffness map values had an average difference of 0.01 ± 0.001 kPa compared to the quantification results derived from the color-box provided by the built-in software of the ultrasound system. Highest classification accuracy from the SVM model was 87.0% with sensitivity and specificity values of 83.3% and 89.1%, respectively. Receiver operating characteristic curves analysis gave an area under the curve value of 0.85 with [0.77-0.89] confidence interval. CONCLUSIONS: The proposed CAD system employing color to stiffness mapping and classification algorithms offered superior results, comparing the already published clinical studies. It could prove to be of value to physicians improving the diagnostic accuracy of CLD and can be employed as a second opinion tool for avoiding unnecessary invasive procedures.
PURPOSE: Classify chronic liver disease (CLD) from ultrasound shear-wave elastography (SWE) imaging by means of a computer aided diagnosis (CAD) system. METHODS: The proposed algorithm employs an inverse mapping technique (red-green-blue to stiffness) to quantify 85 SWE images (54 healthy and 31 with CLD). Texture analysis is then applied involving the automatic calculation of 330 first and second order textural features from every transformed stiffness value map to determine functional features that characterize liver elasticity and describe liver condition for all available stages. Consequently, a stepwise regression analysis feature selection procedure is utilized toward a reduced feature subset that is fed into the support vector machines (SVMs) classification algorithm in the design of the CAD system. RESULTS: With regard to the mapping procedure accuracy, the stiffness map values had an average difference of 0.01 ± 0.001 kPa compared to the quantification results derived from the color-box provided by the built-in software of the ultrasound system. Highest classification accuracy from the SVM model was 87.0% with sensitivity and specificity values of 83.3% and 89.1%, respectively. Receiver operating characteristic curves analysis gave an area under the curve value of 0.85 with [0.77-0.89] confidence interval. CONCLUSIONS: The proposed CAD system employing color to stiffness mapping and classification algorithms offered superior results, comparing the already published clinical studies. It could prove to be of value to physicians improving the diagnostic accuracy of CLD and can be employed as a second opinion tool for avoiding unnecessary invasive procedures.
Authors: François Destrempes; Marc Gesnik; Boris Chayer; Marie-Hélène Roy-Cardinal; Damien Olivié; Jeanne-Marie Giard; Giada Sebastiani; Bich N Nguyen; Guy Cloutier; An Tang Journal: PLoS One Date: 2022-01-27 Impact factor: 3.240